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2026-08-16 04:07:21 +02:00
parent 5f95ad7361
commit 799a1c305a
5 changed files with 1826 additions and 0 deletions
@@ -0,0 +1,104 @@
+import camelot
+import pandas as pd
+import re
+
+def extract_bank_data(file_path):
+    print(f"Reading {file_path}... shifting Payment Details parsing one row lower.")
+    
+    # We use 'stream' flavor for the NLB report layout
+    tables = camelot.read_pdf(file_path, pages='1-end', flavor='stream')
+    
+    final_transactions = []
+    current_tx = None
+    
+    # Patterns
+    iban_pattern = r'SI56\s?\d{4}\s?\d{4}\s?\d{4}\s?\d{3}'
+    amount_pattern = r'=(\d+[\.,]\d{2})'
+    date_pattern = r'(\d{2}\.\d{2}\.\d{2})'
+    bic_pattern = r'\b[A-Z0-9]{8,11}\b'
+
+    for table in tables:
+        df = table.df
+        for _, row in df.iterrows():
+            cells = [str(c).strip() for c in row]
+            row_text = " ".join(cells)
+            
+            amount_match = re.search(amount_pattern, row_text)
+            date_match = re.search(date_pattern, row_text)
+            
+            # TRIGGER: New transaction starts
+            if amount_match and date_match:
+                if current_tx:
+                    final_transactions.append(current_tx)
+                
+                amt_val = amount_match.group(1)
+                
+                # Debit/Credit alignment logic
+                is_credit = False
+                for i, c in enumerate(cells):
+                    if '=' in c and i >= 2:
+                        is_credit = True
+                
+                current_tx = {
+                    "value date": date_match.group(1),
+                    "Creditor / Debtor": cells[0].split('\n')[0].strip(),
+                    "account": "",
+                    "payment details": "",
+                    "debit": "" if is_credit else amt_val,
+                    "credit": amt_val if is_credit else "",
+                    "skip_rows": 0  # Counter to shift parsing lower
+                }
+            
+            elif current_tx:
+                text_line = cells[0]
+                if not text_line: continue
+
+                # 1. Identify IBAN
+                iban_search = re.search(iban_pattern, text_line)
+                if iban_search:
+                    current_tx["account"] = iban_search.group(0).replace(" ", "")
+                    # When IBAN is found, the line below it is usually BIC. 
+                    # We set skip_rows to 1 to skip that BIC line.
+                    current_tx["skip_rows"] = 1
+                    continue
+
+                # 2. Skip the "BIC" line (one row lower logic)
+                if current_tx["skip_rows"] > 0:
+                    current_tx["skip_rows"] -= 1
+                    continue
+
+                # 3. Capture Payment Details (after skipping)
+                # Filter out obvious address noise
+                is_address = any(k in text_line.upper() for k in ["CESTA", "ULICA", "TRG", "LJUBLJANA"]) or re.search(r'\b\d{4}\b', text_line)
+                
+                if not is_address and len(text_line) > 2:
+                    # Scrub labels and internal bank noise
+                    clean_text = re.sub(r'^(purpose|payment details|reference)', '', text_line, flags=re.I).strip()
+                    clean_text = re.sub(r'\b[CD]R\d+\b|\bNRC\b', '', clean_text).strip()
+                    
+                    if clean_text:
+                        # Append to details
+                        if not current_tx["payment details"]:
+                            current_tx["payment details"] = clean_text
+                        else:
+                            current_tx["payment details"] += " " + clean_text
+
+    if current_tx:
+        final_transactions.append(current_tx)
+
+    # Export
+    df_final = pd.DataFrame(final_transactions).drop(columns=['skip_rows'])
+    cols = ["value date", "Creditor / Debtor", "account", "payment details", "debit", "credit"]
+    df_final = df_final[cols]
+    
+    # Final cleanup
+    for col in df_final.columns:
+        df_final[col] = df_final[col].astype(str).str.replace(r'\s+', ' ', regex=True).str.strip()
+        df_final[col] = df_final[col].replace('nan', '')
+
+    output_file = "bank_export_final_fixed.csv"
+    df_final.to_csv(output_file, sep=';', index=False, encoding='utf-8-sig')
+    print(f"Success! {len(df_final)} transactions exported.")
+
+if __name__ == "__main__":
+    extract_bank_data("MSDataReport (1).pdf")